AN EFFICIENT CONTENT BASED YOGA ASANA RETRIEVAL SYSTEM FOR TREATING MUSCULAR DISORDERS
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Abstract
Due to the increasing prevalence of muscular disorders linked to modern lifestyles, yoga has regained attention for its therapeutic benefits, yet accurate and efficient pose recognition remains a challenge. This paper presents CBYAR-Net, a novel content-based yoga asana retrieval framework designed to address key challenges such as variability in asana appearance, dataset scarcity, and high computational demands. The proposed system integrates Structural Detail Descriptor (SDD) and Spatial Color Distribution Descriptor (SCDD) to capture fine-grained structural features and spatial color patterns, while an unsupervised SVM (U-SVM) clusters similar feature vectors for efficient retrieval. Experimental results demonstrate that CBYAR-Net outperforms traditional methods like KNN and Cosine Similarity, achieving 96.15% retrieval accuracy. The framework provides a robust, scalable, and computationally efficient solution for automated yoga pose recognition and retrieval
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REFERENCES
[1] Garg, R. K. The alarming rise of lifestyle diseases and their impact on public health: A comprehensive overview and strategies for overcoming the epidemic. Journal of Research in Medical Sciences, vol. 30, no. 1, Jan. 2025. DOI: 10.4103/jrms.jrms_54_24.
[2] Yatham, P., Chintamaneni, S., Stumbar, S. E. Lessons from India: A narrative review of integrating yoga within the US healthcare system. Cureus, Aug. 2023. DOI: 10.7759/cureus.43466.
[3] Kumawat, J., Metri, K. G. Research on yoga for stress management: Bibliometric trends from 2000 to 2024. Journal of Ayurveda and Integrative Medicine, vol. 16, no. 4, p. 101163, Jul. 2025. DOI: 10.1016/j.jaim.2025.101163.
[4] Meghana, J. H., Chethan, H. K., Kumar, K. S. S., Prakash, S. P. S. Comprehensive analysis of pose estimation and machine learning classifiers for precise yoga pose detection and classification. Procedia Computer Science, vol. 258, pp. 3345–3356, Jan. 2025. DOI: 10.1016/j.procs.2025.04.592.
[5] Nandyal, S. S. D. S. A dataset to find effect of yogasan on muscular disorder. International Journal of Advanced Research in Engineering and Technology (IJARET), Jun. 12, 2021. URL: https://iaeme.com/Home/article_id/IJARET_12_06_001 (Visited on 03.10.2025).
[6] Palanimeera, J., Ponmozhi, K. Yoga posture recognition by learning spatial-temporal feature with deep learning techniques. International Journal of Image and Graphics, Jul. 2023. DOI: 10.1142/s0219467824500554.
[7] Talaat, A. S. Novel deep learning models for yoga pose estimator. SN Applied Sciences, vol. 5, no. 12, Nov. 2023. DOI: 10.1007/s42452-023-05581-8.
[8] Dhanyal, S. S., Nandya, S. S. Yoga pose annotation and classification by using time-distributed convolutional neural network. Indonesian Journal of Electrical Engineering and Computer Science, vol. 32, no. 3, p. 1639, Dec. 2023. DOI: 10.11591/ijeecs.v32.i3.pp1639-1647.
[9] Rajendran, A. K., Sethuraman, S. C. YogiCombineDeep: Enhanced yogic posture classification using combined deep fusion of VGG16 and VGG19 features. IEEE Access, vol. 12, pp. 139165–139180, Jun. 2024. DOI: 10.1109/ACCESS.2024.3414654.
[10] Bhandage, V., Prabhu, S., Hadimani, B. S., Chadaga, K., Sampathila, N., Shetty, S. Classification of Surya Namaskar yoga asanas: A sequential combination of predominant poses for physical and mental health. IEEE Access, vol. 12, pp. 102027–102034, Jul. 2024. DOI: 10.1109/ACCESS.2024.3429569.
[11] Yeh, S.-C., Yang, C.-K. Yoga pose recognition and motion analysis for a home-based fitness monitoring and health management system. Signal, Image and Video Processing, vol. 19, no. 10, Jul. 2025. DOI: 10.1007/s11760-025-04436-6.
[12] Özsezer, G., Mermer, G. Real‐time prediction of correct yoga asanas in healthy individuals with artificial intelligence techniques: A systematic review for nursing. Nursing Open, vol. 12, no. 8, Aug. 2025. DOI: 10.1002/nop2.70278.